ArticlePDF Available

Effects of the Preference for Environmental Quality on the Export Competition between China and OECD Countries

Authors:

Abstract

This study examines how importers’ preferences for environmentally friendly products influence the effect of China's export growth on the exports of OECD countries to third markets. The effect of China's export growth is systematically investigated using the theoretical gravity model, which assumes that importers’ environmental preferences are heterogeneous among countries. A new measure is also proposed to represent importers’ revealed preferences for environmental quality across countries. Panel data consisting of observations for 30 OECD exporting countries and 60 importing countries over the 2000–2010 period confirm that the crowding‐out effect of China's export growth on the exports of OECD countries observed in markets for consumption goods and the dampening effect observed in markets for intermediate goods are becoming weaker as the importer preference for environmental quality becomes stronger. This article is protected by copyright. All rights reserved.
ORIGINAL ARTICLE
Effects of the preference for environmental quality
on the export competition between China and
OECD countries
Jung Joo La
Division of Industrial Organization Research, Pi-Touch Institute, Seoul, Korea
KEYWORDS
Chinas crowding-out effect, Chinas dampening effect, preference for environmental quality
1
|
INTRODUCTION
Chinas export growth rate increased rapidly from 6.8% in 2001 to 31.3% in 2010 after it joined
the World Trade Organization (WTO). As a result, there is growing concern that the expansion of
Chinas exports may have negative effects on other countriesexports. Studies in this area have
focused mainly on the competition between China and other Asian countries (Eichengreen, Rhee,
& Tong, 2007; Greenaway, Mahabir, & Milner, 2008; Ianchovichina & Walmsley, 2005; Lall &
Albaladejo, 2004; Shafaeddin, 2004). Thus, the question of whether Chinas export growth has a
negative effect on the exports of Organisation for Economic Cooperation and Development
(OECD) countries remains unanswered.
According to Greenaway et al. (2008), Chinas export growth is expected to have a negative
effect on highincome countriesexports to third markets. Greenaway et al. (2008) stressed that
because Chinas comparative advantage comes from moving from the production of lowtechnol-
ogy, lowskilled intensive goods to high valueadded and less labourintensive manufacturing, its
export growth is having a strong displacement effect on highincome Asian exporters. However,
this negative effect may not be applicable to intermediate goods. Therefore, a disaggregated, secto-
rial study is needed to reflect the increasing crosscountry complementarity of production processes
that accompany Chinas rapid integration into the global production network. According to Eichen-
green et al. (2007), Chinas export growth has had a positive effect on the exports of other Asian
countries in third markets for intermediate goods. In accordance with Athukorala (2009), this effect
can be defined as a dampening effect, in that Chinas exports grow faster than those of its
-------------------------------------------------------------------------------------------------------------------------------------
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.
© 2018 The Authors. The World Economy Published by John Wiley & Sons Ltd
Received: 18 November 2013
|
Revised: 19 August 2018
|
Accepted: 27 August 2018
DOI: 10.1111/twec.12732
World Econ. 2018;120. wileyonlinelibrary.com/journal/twec
|
1
competitors and retard their growth.
1
Based on prior studies, the crowdingout or dampening
effects of Chinas export growth on the exports of OECD countries are expected.
However, the effects of Chinas export growth on those of OECD countries may be weaker
because importers are expected to select exports from OECD countries rather than from China when
they have a strong preference for environmental quality. According to Copeland and Taylor (1994),
higher income countries specialise in producing relatively clean goods. Thus, OECD countries are
expected to export more environmentally friendly goods than China. According to Barbozas (2007)
report, Chinas goods are vulnerable to contamination because of long supply chains with multiple
contractors and subcontractors. This difference in the environmental quality of goods gives rise to the
variation in the preferences for environmental quality across the importing countries. Consequently,
the objective of this study is to analyse how importerspreferences for environmentally friendly prod-
ucts influence the effect that Chinas export growth has on the exports of OECD countries.
The gravity model has been used to investigate the effects of Chinas exports in a number of prior
studies (e.g., Athukorala, 2009; Eichengreen et al., 2007; Greenaway et al., 2008). However, these
studies do not use a theoretical gravity model to analyse Chinas exports; instead, they simply add
exports as a factor into the gravity model. Thus, this study presents a new approach to examining the
effect of Chinas export growth on the exports of OECD countries by extending the theoretical gravity
model developed by Anderson and van Wincoop (2003). Specifically, the effect of Chinas exports is
incorporated as a component of trade cost into the theoretical gravity equation, which is derived from
general equilibrium analysis. In addition, the unobserved multilateral resistance factors, which are
critical to the model of Anderson and van Wincoop (2003), are examined using the simple firstorder
loglinear Taylorseries expansion method proposed by Baier and Bergstrand (2009).
Another key innovation of this study is to ease the constraint of the assumption that importers
preferences for environmentally friendly products are the same across countries and to propose a
new measure that represents importersactual preferences for environmental quality across coun-
tries. The existing studies in this area implicitly assume that importerspreferences are the same
and do not consider them to be a significant factor.
The remainder of this paper is organised as follows. Section 2 provides an overview of the
exports of China and selected OECD countries. Section 3 establishes a simple model that serves as
the theoretical framework for the study. Section 4 provides empirical evidence on the influence
that importerspreferences for environmental quality have on the effect of Chinas export growth
on the exports of OECD countries based on the new indicator relating to importerspreferences
for environmentally friendly products. Section 5 concludes the paper.
2
|
EXPORTS OF CHINA AND OECD COUNTRIES
Figure 1 presents the trends in export growth from 2000 to 2010 for China and OECD countries
to the third markets by sector.
2
Chinas exports increased rapidly across all sectors after it joined
the WTO in 2001, whereas OECD countriesexports grew slowly in this period. Thus, Chinas
1
In other words, the dampening effect means that the exports of Chinas competitors grow below unity due to the rapid
growth of Chinas exports. Lall and Albaladejo (2004) regarded this dampening effect as a partial threat.
2
The three sectors are sorted in the HS 96 version according to the classification of Eichengreen et al. (2007) based on
SITC revision 2 as follows: Capital goods fall under the 84, 85(), 86, 87(), 88 and 89 codes; consumption goods are
defined as including the 0124, 30, 6166, 85278528, 8703, 87118713, 9092 and 9497 codes; and intermediate goods
comprise the 2529, 3160, 6783, 93 and 99 codes.
2
|
LA
export growth appears to have crowded out or dampened that of OECD countries, depending on
the sector. Across the three sectors examined, the most significant gap between China and OECD
countries during the period appears in relation to capital goods, of which Chinas exports jumped
sharply from $1,169 million in 2000 to $13,014 million in 2010, whereas those of OECD coun-
tries rose moderately from $1,003 million to $1,425 million on average over the same period.
Hence, it is predicted that the crowdingout effect of Chinas export growth on the exports of
OECD countries, if it exists, is the most severe in this sector.
Table 1 shows the top five export destinations for China and a number of representative OECD
countries based on the average export volumes from 2000 and 2010. China is included in the top five
export markets of almost all of the selected OECD countries for intermediate goods and capital goods,
whereas it is only in the top export market of Japan for consumption goods. OECD countriesexports
to China account for 10.7%, 7.2% and 4.1% of their total exports on average for intermediate goods,
capital goods and consumption goods, respectively, while their average exports to each market
account for 0.4% on average across all sectors, suggesting that China supports the export growth of
OECD countries through its imports from them, especially of intermediate goods and capital goods.
Chinas top five export destinations overlap with those of the selected OECD countries across
all sectors, except for South Korea for consumption goods and Germany and the Netherlands for
capital goods, although these three exceptions do belong within the other OECD countriestop 10
export markets.
3
Chinas top five export markets account for 55.9%, 45.2% and 56.4% of its total
exports of consumption goods, intermediate goods and capital goods, respectively. OECD
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Unit: Million Dollars
China (Consumption goods)
China (Intermediate goods)
China (Capital goods)
OECD Ave (Consumption goods)
OECD Ave (Intermediate goods)
OECD Ave (Capital goods)
FIGURE 1 Trend of export growth for China and OECD countries to third markets
Note: The value of each year is the average of China and OECD countries across third markets and the gray
lines represent the average levels of 30 OECD countries.
Source: The authors own estimates based on the UNCOMTRADE data set.
3
South Korea and Germany are included in the top 10 export markets of Australia, Japan and the United States for con-
sumption goods and capital goods, and the Netherlands is a top 10 export market of Germany and Japan for capital goods.
LA
|
3
countriesexports to the corresponding destinations account for 29.4%, 21.7% and 21.6%, on aver-
age, respectively. These figures indicate that there is intense export competition between China
and OECD countries.
Figure 2 shows the trend in the Export Similarity Index (ESI) between China and OECD coun-
tries.
4
First used by Finger and Kreinin (1979), this measure is a good proxy for the level of
export competition between China and OECD countries in third markets. The value for each year
TABLE 1 Top five export destinations of China and selected OECD countries by sector
Rank
China Australia Germany Japan USA
Market Value Market Value Market Value Market Value Market Value
Consumption goods
1 USA 53 Japan 3 USA 31 USA 50 Canada 45
2 Japan 33 USA 3 UK 24 China 8 Mexico 22
3 Hong Kong 26 New
Zealand
2 France 22 Germany 6 Japan 21
4 Germany 9 Saudi
Arabia
1 Italy 22 Australia 5 Germany 13
5 S. Korea 8 UK 1 Belgium 20 Hong
Kong
5UK 10
Intermediate Goods
1 USA 35 Japan 15 France 34 China 33 Canada 72
2 Hong Kong 25 China 14 Netherlands 26 USA 22 Mexico 47
3 Japan 19 S. Korea 7 Italy 24 S. Korea 22 China 19
4 S. Korea 16 India 6 Austria 23 Hong
Kong
11 Japan 17
5 India 7 UK 3 UK 22 Thailand 8 UK 15
Capital Goods
1 Hong Kong 73 USA 1 France 40 USA 57 Canada 87
2 USA 71 New
Zealand
1 USA 30 China 40 Mexico 53
3 Japan 27 Singapore 0.4 UK 24 Hong
Kong
18 Japan 19
4 Germany 18 China 0.4 Italy 19 S. Korea 18 China 18
5 Netherlands 16 Papua New
Guinea
0.3 China 19 Thailand 12 UK 17
Notes: The figures are the average values between 2000 and 2010 and are measured in billions of dollars. The selected OECD
countries represent major regions such as the Oceania, Europe, Asia and North America.
Source: The authors own estimates based on the UNCOMTRADE data set.
4
The Export Similarity Index is computed as follows:
ESIðcj;iÞ¼ Min½XrðciÞ;XrðjiÞ
fg
100;
where X
r
(ci) is the share of product rin the exports of country cto country iand X
r
(ji) is the share of product r in the
exports of country jto country i.
4
|
LA
represents the average of the ESIs between China and the 30 OECD countries and is calculated
based on the HS 96 version 6digit codes. Among the three sectors, the level of export competition
between China and OECD countries is the highest for capital goods and the lowest for consump-
tion goods. In regard to the dynamic pattern of export competition, there is little change for con-
sumption goods during the period of analysis. However, the export competition for capital goods
and intermediate goods is becoming increasingly fierce. These figures are consistent with the
observation that Chinas exports are moving from low valueadded to high valueadded goods with
Chinas rapidly increasing participation in the global production networks.
3
|
THE MODEL
This study extends the theoretical gravity model developed by Anderson and van Wincoop (2003)
to investigate how importerspreferences for environmentally friendly products influence the effect
of Chinas export growth on the exports of OECD countries. The traditional gravity model used in
the literature lacks a theoretical foundation, mainly due to the absence of multilateral resistance
factors, which represent the barriers to trade that each country faces with its trading partners.
Accordingly, Anderson and van Wincoop (2003) develop a new theoretical gravity model to con-
trol for these factors under the assumption that each country specialises in the production of only
one good, which is differentiated by place of origin, and that consumer preferences are identical
and homothetic. The following demand equation is derived from Anderson and van Wincoops
(2003) utility maximisation problem:
15
20
25
30
35
40
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Unit: %
Consumption Goods
Intermediate Goods
Capital Goods
FIGURE 2 Trend of Export Similarity Index between China and OECD countries
Note: The value of each year is the average of 30 OECD countries and is calculated on the basis of HS 96
version 6-digit codes.
Source: The authors own estimates based on the UNCOMTRADE data set.
LA
|
5
Xei ¼yeyi
yw
tei
QePi

1σ
;(1)
where:
Ye¼i
tei
Pi

1σ
θi
"#()
1
1σ
;(2)
Pi¼e
tei
Qe

1σ
θe
"#()
1
1σ
;(3)
X
ei
is the bilateral trade flow from country eto country i;y
e
(y
i
) is the income of exporting county
e(importing country i); y
w
is the global income; t
ei
is the trade cost between eand i;θ
e
(θ
i
)=y
e
/
y
w
(y
i
/y
w
); and σ= 1/(1ρ) is the elasticity of substitution among different goods.
Under the assumption that trade costs are symmetric, t
ei
=t
ie
, a solution to Equations (2) and (3)
is Π
e
=P
e
. Then, taking logarithms on both sides of Equation (1), the following equation is derived:
ln Xei ¼αþln yeþln yiðσ1Þln tei þðσ1Þln Peþðσ1Þln Pi;(4)
where P
e
(P
i
) is the CES price index of exporting country e(importing country i), which repre-
sents an unobserved multilateral resistance factor.
Although there are several methods for accounting for unobserved multilateral resistance terms,
the simple firstorder loglinear Taylorseries expansion approach proposed by Baier and Berg-
strand (2009) is the most appropriate one for panel data estimation. The custom nonlinear least
squares model introduced by Anderson and van Wincoop (2003) could be computationally chal-
lenging with panel data, and the fixed effect alternative cannot control for timevarying multilateral
resistance factors (Awokuse & Yin, 2010).
5
Thus, this study follows Baier and Bergstrand (2009)
in representing the multilateral resistance terms as follows:
ln Pe¼N
i¼1θiln tei 1
2N
k¼1N
m¼1θkθmln tkm;(5)
ln Pi¼N
e¼1θeln tei 1
2N
k¼1N
m¼1θkθmln tkm:(6)
Substituting these derived equations into Equation (4) yields:
ln Xei ¼αþln yeþln yiðσ1Þln tei þðσ1ÞN
k¼1θkln tek þðσ1ÞN
k¼1θkln tki
ðσ1ÞN
k¼1N
m¼1θkθmln tkm:(7)
In this study, the unobservable trade cost is modelled as the following function of the observ-
able variables:
tei ¼DISβ1
ei eðβ2CTeiþβ3CLei þβ4COei ÞCIeðln
~
Eln EiÞ
i;Ie¼1ifeis an OECD country
0 otherwise;
(8)
where DIS
ei
is the bilateral distance between country eand country i;CT
ei
,CL
ei
, and CO
ei
are
dummy variables indicating whether the countries are contiguous, share a common official language
and have ever had a colonial link, respectively. In addition, C
i
is the exports from China to country
5
Although Baier and Bergstrand (2007) devise countryandtime effects as extended fixed effects to account for the time
varying multilateral resistance terms, this method leads to overly controlled timevarying countryspecific variables, includ-
ing those of interest in this study.
6
|
LA
i,I
e
is an indicator function taking the value of one if exporting country eis an OECD country and
zero otherwise, E
i
is a measure of importing country is preference for environmentally friendly
exported goods, and
~
Eis the average global level of the preference for environmental quality.
6
Except for CIeðln
~
Eln EiÞ
i, Equation (8) is a loglinear function of the observable variables derived
from the literature (Anderson & van Wincoop, 2003; Baier & Bergstrand, 2009; Hallak, 2006). The
novelty in specifying the unobservable trade cost relates to the influence of Chinas exports to coun-
try i, the intensity of which is determined by the level of importing country is preference for envi-
ronmental quality relative to the average global level. If exporting country eis not an OECD
country, then the trade cost function follows that in the literature, which does not consider the varia-
tion in the preference for environmentally friendly goods across importing countries.
As Chinas export growth to country ican impede or enhance the exports of OECD countries to
the same markets, as explained, this factor is included in the trade cost function. The level of import-
ing country is preference for environmental quality is also added to reflect the influence of the varia-
tion in preferences across importing countries on the export competition between China and OECD
countries. The difference between China and OECD countries in the environmental quality of exports
gives rise to the variation in the preferences for environmental quality across importing countries.
Consequently, an importer with a strong preference for environmental quality is likely to exhibit a
greater demand for exports from OECD countries than for those from China. Therefore, the effect of
Chinas export growth on the exports of OECD countries is assumed to be affected by the level of
importing country is preference for environmental quality E
i
relative to the world average level
~
E.
Provided that country eis an OECD country, inserting the trade cost from Equation (8) into
Equation (7) generates the following gravity equation:
ln Xei ¼αþln yeþln yiðσ1Þðln
~
Eln EiÞln Ciðσ1Þβ1ln DISei
þðσ1Þβ2CTei þðσ1Þβ3CLei þðσ1Þβ4COei þðσ1Þln
~
EMRCi
ðσ1ÞMRECiþðσ1Þβ1MRDISei ðσ1Þβ2MRCTei
ðσ1Þβ3MRCLei ðσ1Þβ4MRCOei;
(9)
where:
7
MRCi¼N
k¼1θkln CkþN
m¼1θmln CiN
k¼1N
m¼1θkθmln Cm;
MRECi¼N
k¼1θkln Ekln CkþN
m¼1θmln Eiln CiN
k¼1N
m¼1θkθmln Emln Cm;
MRDISei ¼N
k¼1θkln DISek þN
m¼1θmln DISmi N
k¼1N
m¼1θkθmln DISkm
hi
;
MRCTei ¼N
k¼1θkCTek þN
m¼1θmCTmi N
k¼1N
m¼1θkθmCTkm
hi
:
The fourth term on the righthand side of Equation (9), ðσ1Þðln
~
Eln EiÞln Ci, shows that the
effect of Chinas export growth on OECD countriesexports to third markets is influenced by the
importers preference for environmentally friendly products, given that σand
~
Eare constant.
Where environmental preferences are the same across importers, or Ei¼
~
E, Equation (9) follows
6
Environmental quality represents the cleanness of production as defined by Amacher, Koskela, and Ollikainen (2004). The
cleanness of production implies environmentally friendly production and the use of less polluting inputs.
7
MRCL
ei
and MRCO
ei
follow the pattern of MRCT
ei
.
LA
|
7
the gravity model of Anderson and van Wincoop (2003) and the variable for Chinas exports lnC
i
disappears. Thus, the practice of simply adding Chinas exports to the gravity model as conducted
in prior studies is implausible in this theoretical context.
The influence of importerspreferences for environmental quality on the effect of Chinas
export growth on the exports of OECD countries is expected to be valid only in relation to con-
sumption goods and intermediate goods and not capital goods. Consumers may demand more envi-
ronmentally friendly goods to maximise their utility when purchasing consumption goods, given
their increasing preference for environmental quality.
8
Therefore, consumption goods can be
included in the environmental demand sector. Although the users of intermediate goods are firms,
these inputs are closely associated with consumption goods, as they are mostly used to produce
consumption goods. Thus, intermediate goods can be included in the quasienvironmental demand
sector. The buyers of capital goods take profit maximisation into account by reducing production
costs rather than maximising consumer utility, as they are firms. Hence, capital goods can be trea-
ted as part of the nonenvironmental demand sector. Therefore, the argument regarding importers
preferences for environmental quality is only applicable to the environmental demand and quasi
environmental demand sectors, as these sectors are closely related to consumer utility.
4
|
EMPIRICAL ANALYSIS
In this section, a measure is first devised to gauge the level of importer preference for environmen-
tal quality across countries for the regression analysis. The econometric methods for estimating the
specifications of the model established in Section 3 are then outlined, the data used described and
the empirical results presented.
4.1
|
Indicator of importer preference for environmental quality
The importer preference for environmental quality indicator (IPEQI) included in the model builds
on an environmental quality indicator (EQI) that ranks export products according to the environ-
mental protection efforts associated with their production. In constructing the EQI, reference is
made to the PRODY index introduced by Hausmann, Hwang, and Rodrik (2007). The PRODY
index measures the weighted average per capita GDP of countries exporting a given product and
thus represents the income level associated with that product. This index has been used by a num-
ber of recent studies as a proxy for the level of export sophistication (e.g., Jarreau & Poncet, 2012;
Minondo, 2010; Xu & Lu, 2009; Yao, 2009). The indicator is calculated as follows:
PRODYn¼e
ðxen=XeÞ
eðxen=XeÞ

Ye;(10)
where x
en
denotes country es exports of product nand X
e
is country es total exports. In addition,
ðxen=XeÞ=eðxen =XeÞis the revealed comparative advantage of country ein relation to product n
and Y
e
is country es per capita GDP based on its purchasing power parity. Hence, PRODYn repre-
sents a weighted average of Y
e
, where the weights correspond to the revealed comparative
8
Arora and Gangopadhyay (1995) reasoned that the growth in firmsvoluntary overcompliance with environmental regula-
tions is due to consumerspreferences for environmental quality. Hamilton and Zilberman (2006) also stressed that con-
sumerspreferences for market goods are as much determined by the environmental attributes of products as by any other
quality attributes. Thus, the demand of consumers with new attitudes towards environmental values may be driving the mar-
ket for green products (Chen, 2001).
8
|
LA
advantage. According to Hausmann et al. (2007), the rationale for using revealed comparative
advantage as a weight is to ensure that the size of a country does not distort the ranking of its
goods. The fundamental assumption underlying the PRODY index is that countries with a higher
per capita GDP export more sophisticated goods. The same logic can be applied in using the EQI
to complement the environmental performance index (EPI), which is a direct and comprehensive
measure of environmental preservation. That is, a country with a higher EPI exports more environ-
mentally friendly goods. The EQI is constructed as follows:
EQIn¼e
ðxen=XeÞ
eðxen=XeÞ

EPIe:(11)
The EPI is a composite index of the effects of current national environmental protection efforts
devised by the Yale Center for Environmental Law and Policy and the Center for International
Earth Science Information Network at Columbia University, in collaboration with the World Econ-
omic Forum and the Joint Research Center of the European Commission. The EPI has been pub-
lished biannually in a stylised form since 2006. The index builds on measures relevant to two core
objectives: reducing the environmental stress to human health and protecting ecosystems and natu-
ral resources. The second objective is divided into five policy categories: air pollution, water
resources, biodiversity and habitat, productive natural resources, and climate change. The EPI is
aggregated through a weighted average of detailed indicators according to the aforementioned core
objectives and policy categories (e.g., 16 indicators in 2006 and 25 indicators in 2008). The num-
ber of countries covered by the index also varies (e.g., 133 countries in 2006 and 149 countries in
2008). Table 2 reports the average EPI between 2006 and 2008. The value for Sweden, 90.5, is
the highest among the 130 countries, whereas that of Niger, 32.4, is the lowest. The values for
China and OECD countries of interest in this study are 60.6 and 82.6 (average), respectively,
which provides further evidence supporting the argument made in Section 3 that OECD countries
export more environmentally friendly goods than China.
The EQI is now used to construct the IPEQI. The extent of an importers preference for envi-
ronmental quality is revealed by the level of imports of green goods. In other words, the IPEQI is
the weighted average of the EQI, where the weights represent the share of each product in the
countrys total imports. The IPEQI is given as follows:
9
IPEQIi¼n
min
Mi

EQIn;(12)
where m
in
denotes country is imports of product nand M
i
denotes country is total imports.
Table 3 shows the empirical results for the IPEQI as constructed by Equation (12). Note that
because the average EQI between 2006 and 2008 is used to construct the IPEQI, the EQI values
used in constructing the IPEQI do not vary over the years.
10
Each statistic is obtained on the basis
9
The EQI is a measure byproduct and the EPI is a measure by country. The latter pays no regard to the sector, such as con-
sumption or intermediate goods. The IPEQI is a measure by country with a component of the EQI. It is a target measure to
gauge the level of importer preference for environmental quality across countries for the regression analysis. Jarreau and
Poncet (2012) used a similar method to measure the sophistication level of imports by country.
10
Current export values need to be transformed into constants for the average EQI, which requires the export price indices
of each country and product. However, because these data are difficult to obtain, the US import price index used by Min-
ondo (2010) is adopted as a good proxy of the average evolution of global export prices. The US import price indices are
obtained from the U.S. Bureau of Labor Statistics. In addition, the weights in Equation (11) are calculated based on the HS
96 version 6digit codes, which are the most disaggregated level in terms of international trade data.
LA
|
9
of the average IPEQI from 2000 to 2010. The US export price index is used to transform current
import values into constants. Table 3 reveals that Switzerland and Niger record the maximum
(76.87) and minimum (67.01) values, respectively, among the 99 countries for consumption goods,
and Switzerland and India have the respective maximum (76.14) and minimum (69.38) values for
intermediate goods.
11
As explained by Brecard, Hlaimi, Lucas, Perraudeau, and Salladarre (2009), environmentally
friendly products are more expensive than less environmentally friendly goods, because the former
incur additional costs required to develop environmentally friendly production technologies. More-
over, in this study, green goods are regarded as normal, rather than public goods. Thus, the IPEQI
is expected to be constrained by the importers income (Arora & Gangopadhyay, 1995; Brecard et
al., 2009; Franzen, 2003; Torgler & GarciaValinas, 2007). The relationship between income and
the IPEQI is analysed in Table 3 through the correlation coefficient between the logarithm of the
average IPEQI and the logarithm of the average income from 2000 to 2010.
12
The correlation
coefficients between them are positively significant at the 1% level across all sectors. However, the
value for intermediate goods is 0.62, which is unexpectedly low relative to the value of 0.79 for
consumption goods. Trade conducted by multinational corporations provides a possible explanation
for this low value for intermediate goods. According to the study by Helpman (1985), the share of
intrafirm trade increases as the difference in relative factor endowments widens, provided that the
difference is not too large. This finding supports the argument that subsidiaries in developing
TABLE 2 Summary of environmental performance index (EPI) statistics
No. of observations Mean SD Min. Max.
Statistics 130 68.2 13.2 32.4 (Niger) 90.5 (Sweden)
Selected 60.6 (China) 82.6 (OECD Average)
Note: Each statistic is obtained on the basis of the average EPI between 2006 and 2008.
Source: The authors own estimates based on the environmental performance index data set.
TABLE 3 Summary of IPEQI statistics
Consumption goods Intermediate goods
Mean 74.25 73.70
SD 1.75 1.35
Min. 67.01 (Niger) 69.38 (India)
Max. 76.87 (Switzerland) 76.14 (Switzerland)
Correl. Coeff. 0.79*** 0.62***
No. of observations 99 99
Notes: Each statistic is obtained on the basis of the average IPEQI from 2000 to 2010. Correl. Coeff. denotes the correlation coeffi-
cient between the logarithm of the average IPEQI and the logarithm of the average income.
***Significance at the 1% level.
Source: The authors own estimates based on the above EQIs and the UNCOMTRADE data set
11
India is unexpectedly ranked 99th for intermediate goods because it is highly dependent on environmentally poor natural
resources. The items above 5% in terms of the average share of Indias total imports for intermediate goods from 2000 to
2010 are crude oil (30.3%), unwrought gold (9.2%) and unworked diamonds (7.8%).
12
The income is real GDP per capita measured at purchasing power parity in constant 2000 US dollars.
10
|
LA
countries import ecofriendly intermediate inputs from their parent firms in developed countries to
export finished goods. Therefore, the IPEQIs for developing countries are overestimated, which
leads to the low value of the correlation coefficient for intermediate goods.
4.2
|
Econometric method and data description
The specification for estimating Equation (9) using panel data is as follows:
ln Xeit ¼α0þα1ln yet þα2ln yit þα3ln Cit þα4ln Eit ln Cit
þα5ln DISei þα6CTei þα7CLei þα8COei þMRCit
þMRECit þMRDISeit þMRCTeit þMRCLeit þMRCOeit
þϕeþϕiþϕtþɛeit ;
(13)
where ϕ
e
and ϕ
i
represent exporter and importer effects, respectively, which control for all time
invariant country characteristics, and ϕ
t
denotes time effects, which account for omitted variables
that are common to all trade flows but vary over time.
As noted in the literature, the estimated parameters α
1
,α
2
and α
6
α
8
are expected to have a posi-
tive sign such that the relevant variables act as tradestimulating factors, whereas the parameter α
5
is
likely to have a negative sign, as distance is a proxy for transportation cost. The estimated parame-
ters α
3
and α
4
of interest in this study are expected to have negative and positive signs, respectively,
in that the negative effect of Chinas export growth on the exports of OECD countries is likely to be
weaker in export destinations with a greater preference for environmental quality, where the demand
for exports from OECD countries will be greater than that for goods from China. In accordance with
Eichengreen et al. (2007), the effect of Chinas export growth on the exports of OECD countries is
expected to be manifested as a crowdingout effect in markets for consumption goods, whereas a
dampening effect is observed in markets for intermediate goods.
13
Thus, the slope of the curve repre-
senting Chinas exports to country i,α
3
+α
4
ln E
it
is likely to have a negative value for consump-
tion goods and a positive value between 0 and 1 for intermediate goods.
Various econometric methods, such as the ordinary least squares (OLS) method, fixed effects,
random effects and HausmanTaylor analyses, can be used to estimate Equation (13). However,
OLS estimation may be biased, as it cannot control for the fixed effects of ϕ
e
,ϕ
i
and ϕ
t
. Fixed
and random effect analyses could provide suitable alternatives to control for such factors. How-
ever, fixed effect analysis is more appropriate than random effect analysis, as these factors are
correlated with the explanatory variables in Equation (13). Nevertheless, fixed effect analysis
cannot provide estimates for timeinvariant variables. The HausmanTaylor analysis can yield
coefficients on timeinvariant variables. The Chinese export variable lnC
it
may be correlated with
the error term, thus causing an endogeneity problem, unless Equation (13) controls for the
effects of other countriesexport expansion on the export growth of OECD countries. In addi-
tion, the exporter and importers income variables and the multilateral resistance terms, which
have incomerelated components, can cause simultaneous bias, as pointed out by Anderson
(1979). The HausmanTaylor estimation can alleviate endogeneity problems by adopting the
appropriate instrumental variables from within the model. Moreover, the Hausman test of over
identification ensures the validity of the instrumental variables. Thus, the null hypothesis of the
13
Although capital goods are not associated with importerspreferences for environmental quality, based on the study by
Greenaway et al. (2008) and the observations presented in Section 2, Chinas export growth is expected to have a crowd-
ingout effect on the export growth of OECD countries in this sector.
LA
|
11
Hausman test based on a comparison of the fixed effect and HausmanTaylor estimators should
not be rejected. Therefore, this study adopts the HausmanTaylor analysis as an econometric
method.
The data used for the regression analyses cover 30 OECD exporting countries and 60 OECD
importing countries over the 200010 period.
14
Importing countries that are not consistent across
the variables are excluded from further analysis. In addition, the period in which the export pat-
tern is investigated follows Chinas accession to the WTO and an appropriate time span is con-
sidered for the EQI, which is used as a timeinvariant indicator in constructing the IPEQI.
Export data are obtained from the UNCOMTRADE data set and the data on real GDP (mea-
sured on a purchasing power parity basis), population and the strictness of environmental regula-
tions from the World Development Indicator data set. Export values are deflated to 2000
constant US dollars using the US import price indices obtained from the U.S. Bureau of Labor
Statistics. Moreover, the logarithm of one plus the export value is taken for the zero export
value. As these values only occupy 0.27% for consumption goods, 0.29% for intermediate goods
and 0.60% for capital goods in the sample, the manipulation does not need a censoring model
for estimation.
The distance data, which are calculated using the latitudes and longitudes of the most
important cities or agglomerations in terms of population, are sourced from the gravity data set
of the CEPII, a French research centre. Table 4 summarises the variables used in the regression
analyses.
4.3
|
Regression results
Table 5 shows the regression results for Equation (13) by sector obtained using the Hausman
Taylor estimation. The coefficients of lnCs are significantly negative at the 1% level for con-
sumption goods and intermediate goods, whereas those of the interaction terms between lnC
and lnIPEQI are significantly positive at the same level. For a specific explanation of these
variables for consumption goods, the slope of the lnCcurve is 0.102, 0.092, 0.090,
0.089 and 0.086 at the minimum (4.254), 1st quartile (4.312), 2nd quartile (4.323), 3rd
quartile (4.329) and 4th quartile (4.348) of the lnIPEQI, respectively. With regard to the expla-
nation for intermediate goods, the slope of the lnCcurve is 0.209, 0.232, 0.237, 0.241 and
0.247 at the minimum (4.229), 1st quartile (4.294), 2nd quartile (4.309), 3rd quartile (4.320)
and 4th quartile (4.337) of the lnIPEQI, respectively. These results suggest that the crowding
out effect of Chinas export growth observed in markets for consumption goods and the damp-
ening effect for intermediate goods on the export growth of OECD countries are weaker in
export destinations with a greater preference for environmental quality.
15
These results are con-
sistent with this studys predictions.
The coefficients on the distance variables are significantly negative at the 1% level across sec-
tors and those on the dummy variables regarding common official language and colonial links are
significantly positive at the 1%5% levels, as expected. Concerning the dummy variable on conti-
guity, there is no robust evidence to support the prediction across sectors. The overidentification
tests conducted for the HausmanTaylor estimation do not reject the null hypothesis across sectors,
thus validating the instrumental variables.
14
See Appendix A for the list of countries used in the analysis. Chile, Estonia, Israel and Slovenia are excluded from the list
of OECD countries, as they joined the group in 2010.
15
See Appendix B for the regression results of capital goods.
12
|
LA
TABLE 4 Summary of variables
Variable lnX
eit
lnGDP
et
lnGDP
it
lnC
it
lnC*lnIPEQI
it
lnDIS
ei
lnPOP
et
lnPOP
it
lnSER
et
lnSER
it
Con. goods
No. of observations 19,470 19,470 19,470 19,470 19,470 1,770 19,470 19,470 19,470 19,470
Mean 17.954 26.665 26.117 20.273 87.563 8.231 16.557 16.505 0.776 0.975
SD 2.845 1.426 1.512 1.925 8.37 1.11 1.521 1.664 0.766 0.9
Min. 0 22.816 22.69 13.714 58.805 4.088 12.547 12.547 4.094 4.094
Max. 25.042 30.087 30.087 25.292 109.471 9.883 19.55 20.926 0 0
Inter. goods
No. of observations 19,470 19,470 19,470 19,470 19,470 1,770 19,470 19,470 19,470 19,470
Mean 18.115 26.665 26.117 20.101 86.547 8.231 16.557 16.505 0.776 0.975
SD 2.857 1.426 1.512 1.946 8.306 1.11 1.521 1.664 0.766 0.9
Min. 0 22.816 22.69 14.745 63.195 4.088 12.547 12.547 4.094 4.094
Max. 25.535 30.087 30.087 24.466 105.083 9.883 19.55 20.926 0 0
LA
|
13
4.4
|
Robustness tests
Table 6 presents the results of the robustness tests for the regression results reported in Table 5.
The interaction terms between lnCand lnpercapitaGDP of an importing country are included in
the specification to ensure that the interaction terms between lnCand lnIPEQI are not driven by
the income factor. The first column for each sector in Table 6 shows the regression result of Equa-
tion (13) extended by the interaction term between lnCand lnpercapitaGDP. The coefficients of
these additional variables are significantly positive at the 1% level, but their magnitudes are rela-
tively trivial. The regression results for the variables of interest are consistent with the benchmark
model.
The population variables of the exporting and importing countries are incorporated into the
specification to account for the factor endowment characteristics, in line with the HecksherOhlin
and nonhomothetic taste factors addressed by Bergstrand (1989), given that the lnGDPs and lnper-
capitaGDPs of exporting and importing countries are equivalent to their lnGDPs and lnPOPs in
the specification. The second column for each sector in Table 6 shows the regression result of
Equation (13) extended by population variables. The signs of the coefficients of these additional
variables are consistent with those in the literature. More importantly, the signs of the coefficients
TABLE 5 Regression results (HausmanTaylor estimation)
(1) Consumption goods
(2) Intermediate goods
Dependent variable: lnX
eit
lnGDP
et
3.342*** 0.950***
(22.73) (6.55)
lnGDP
it
1.940*** 1.212***
(20.12) (11.57)
lnC
it
0.812*** 1.297***
(2.99) (4.45)
lnC*lnIPEQI
it
0.167*** 0.356***
(2.87) (5.77)
lnDIS
ei
1.423*** 1.591***
(31.12) (37.86)
lnCT
ei
0.119 0.152
(0.83) (1.15)
lnCL
ei
0.419*** 0.301***
(3.62) (2.83)
lnCO
ei
0.355** 0.657***
(2.40) (4.83)
No. of observations 19,470 19,470
χ
2
(111) 18,731.08*** 17,034.05***
Overidentification test: χ
2
(19) 0.00 0.00
Notes: The figures in parentheses are zvalues. The values for the MRC, MRCE, MRDIS, MRCT, MRCL, MRCO, exporter
dummy, importer dummy, year dummy and the constant do not appear in the table, although they are included in the analysis.
*Significance at the 10% level, **Significance at the 5% level, ***Significance at the 1% level.
14
|
LA
TABLE 6 Regression results (HausmanTaylor estimation)
Consumption goods
Intermediate goods
Dependent variable: lnX
eit
(1) (2) (3) (1) (2) (3)
lnGDP
et
3.295*** 3.474*** 3.626*** 0.945*** 1.013*** 1.327***
(22.42) (23.62) (23.56) (6.53) (6.96) (8.73)
lnGDP
it
1.222*** 2.011*** 2.118*** 0.884*** 1.309*** 1.473***
(8.94) (20.86) (21.27) (5.75) (12.26) (13.43)
lnC
it
1.254*** 0.769*** 0.745*** 1.462*** 1.242*** 1.309***
(4.53) (2.85) (2.76) (4.92) (4.26) (4.50)
lnC*lnIPEQI
it
0.148** 0.166*** 0.155*** 0.334*** 0.342*** 0.345***
(2.55) (2.88) (2.68) (5.38) (5.54) (5.60)
lnC*
lnpercapitaGDP
it
0.055*** ––0.025*** ––
(7.41) (2.90)
lnDIS
ei
1.423*** 1.423*** 1.423*** 1.591*** 1.591*** 1.591***
(31.12) (31.12) (31.11) (37.86) (37.86) (37.86)
lnCT
ei
0.119 0.119 0.119 0.152 0.152 0.152
(0.83) (0.83) (0.83) (1.15) (1.15) (1.15)
lnCL
ei
0.419*** 0.419*** 0.419*** 0.301*** 0.301*** 0.301***
(3.62) (3.62) (3.62) (2.83) (2.83) (2.83)
lnCO
ei
0.355** 0.355** 0.355** 0.657*** 0.657*** 0.657***
(2.40) (2.40) (2.40) (4.83) (4.83) (4.83)
lnPOP
et
3.508*** 3.712*** 1.723*** 2.145***
(11.30) (11.74) (5.61) (6.87)
lnPOP
it
1.767*** 1.844*** 1.027*** 1.140***
(9.19) (9.54) (5.23) (5.78)
(Continues)
LA
|
15
TABLE 6 (Continued)
Consumption goods
Intermediate goods
Dependent variable: lnX
eit
(1) (2) (3) (1) (2) (3)
lnSER
et
––0.070*** ––0.143***
(3.39) (7.08)
lnSER
it
––0.072*** ––0.099***
(3.90) (5.44)
No. of observations 19,470 19,470 19,470 19,470 19,470 19,470
χ
2
(112, 113, 115) 18,809.19*** 19,022.26*** 19,058.52*** 17,043.20*** 17,099.65*** 17,191.16***
Overidentification test:
χ
2
(20, 21, 23)
0.00 0.00 0.00 0.00 0.00 0.00
Notes: The figures in parentheses are zvalues. The values for the MRC, MRCE, MRDIS, MRCT, MRCL, MRCO, exporter dummy, importer dummy, year dummy and the constant do not appear
in the table, although they are included in the analysis.
*Significance at the 10% level, **Significance at the 5% level, ***Significance at the 1% level.
16
|
LA
of the variables of interest are equal to those of the benchmark model and the magnitudes of the
coefficients change only slightly.
To disentangle the pure effect of importerspreferences for environmental quality on the
export competition between China and OECD countries in third markets, an attempt has been
made to control for the effect of environmental regulations on bilateral trade in the specifica-
tion. To achieve this, the narrow measure regarding the strictness of environmental regulations
adopted by Van Beers and van den Bergh (1997) is incorporated into Equation (13).
16
Van
Beers and van den Bergh (1997) found that the estimated coefficients of the measures for both
exporters and importers were statistically significant and negative, although those for importers
are theoretically positive. The third column for each sector in Table 6 presents the regression
results of Equation (13) extended by the measures of the strictness of environmental regula-
tions. Following the approach of Van Beers and van den Bergh (1997), the population vari-
ables of the exporting and importing countries are incorporated into the extended specification
together. The coefficients on the measures of the strictness of environmental regulations are
consistent with those of Van Beers and van den Bergh (1997). In addition, the regression
results for the variables of interest almost follow those of the benchmark model. In sum, it can
be concluded that the main regression results of this study are indeed robust across various
specifications.
5
|
CONCLUSION
This study makes several significant contributions to the literature. First, in examining the
export competition between China and OECD countries, it assumes that importerspreferences
for environmentally friendly products are heterogeneous among countries. Second, a new
measure is proposed to represent importersrevealed preferences for environmental quality
across countries. Third, the theoretical gravity model is used to systemically investigate the
effect of Chinas export growth on the exports of OECD countries in third markets.
OECD countries can regard Chinas export growth as both a threat and an opportunity. As
the results of this study indicate, the threat is manifested in direct crowding out in markets for
consumption goods and partial dampening in markets for intermediate goods. The crowdingout
and dampening effects of Chinas export growth on the exports of OECD countries present
significant obstacles to achieving export equality. However, a good way to reduce the export
inequality arising from the threat posed by China is to deal with the variation in importers
preferences for environmental quality, in that the crowdingout and dampening effects are
weaker in export destinations where importers have greater preferences for environmental qual-
ity. This finding is confirmed by the panel data of observations for the 30 OECD exporting
countries and the 60 importing countries over the 200010 period. Providing that China is
slower in catching up with OECD exporters, the inequality of export growth is expected to
narrow as the export markets in which consumers have strong preferences for environmental
quality expand.
16
As the country coverage of this study is broader than that of Van Beers and van den Bergh (1997), energy use (kg of oil
equivalent) per US$1,000 GDP (constant 2005 PPP) obtained from the World Development Indicator data set is used to
derive the measure of the strictness of environmental regulations. In addition, the base year for the change of energy inten-
sity is 1995 instead of 1980 due to data limitations.
LA
|
17
ORCID
Jung Joo La http://orcid.org/0000-0003-1507-8698
REFERENCES
Amacher, G. S., Koskela, E., & Ollikainen, M. (2004). Environmental quality competition and ecolabeling. Journal
of Environmental Economics and Management,47(2), 284306. https://doi.org/10.1016/S0095-0696(03)00078-0
Anderson, J. E. (1979). A theoretical foundation for the gravity equation. American Economic Review,69(1), 106116.
Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Econ-
omic Review,93(1), 170192. https://doi.org/10.1257/000282803321455214
Arora, S., & Gangopadhyay, S. (1995). Toward a theoretical model of voluntary overcompliance. Journal of Econ-
omic Behavior and Organization,28(3), 289309. https://doi.org/10.1016/0167-2681(95)00037-2
Athukorala, P.-C. (2009). The rise of China and East Asian export performance: Is the crowdingout fear warranted?
World Economy,32(2), 234266. https://doi.org/10.1111/j.1467-9701.2008.01151.x
Awokuse, T. O., & Yin, H. (2010). Does stronger intellectual property rights protection induce more bilateral trade? Evi-
dence from ChinasimportsWorld Development,38(8), 10941104. https://doi.org/10.1016/j.worlddev.2009.12.016
Baier, S. L., & Bergstrand, J. H. (2007). Do free trade agreements actually increase membersinternational trade?
Journal of International Economics,71(1), 7295. https://doi.org/10.1016/j.jinteco.2006.02.005
Baier, S. L., & Bergstrand, J. H. (2009). Bonus vetus OLS: A simple method for approximating international trade
cost effects using the gravity equation. Journal of International Economics,77(1), 7785. https://doi.org/10.
1016/j.jinteco.2008.10.004
Barboza, D. (2007). Scandal and suicide in China: A dark side of toys. New York Times, August 23. Retrieved from:
https://www.nytimes.com/2007/08/23/business/worldbusiness/23suicide.html
Bergstrand, J. H. (1989). The generalized gravity equation, monopolistic competition, and the factorproportions the-
ory in international trade. Review of Economics and Statistics,71(1), 143153. https://doi.org/10.2307/1928061
Brecard, D., Hlaimi, B., Lucas, S., Perraudeau, Y., & Salladarre, F. (2009). Determinants of demand for green prod-
ucts: An application to ecolabel demand for fish in Europe. Ecological Economics,69(1), 115125. https://doi.
org/10.1016/j.ecolecon.2009.07.017
Chen, C. (2001). Design for the environment: A qualitybased model for green product development. Management
Science,47(2), 250263. https://doi.org/10.1287/mnsc.47.2.250.9841
Copeland, B. R., & Taylor, M. S. (1994). NorthSouth trade and the environment. Quarterly Journal of Economics,
109(3), 755787. https://doi.org/10.2307/2118421
Eichengreen, B., Rhee, Y., & Tong, H. (2007). China and the exports of other Asian countries. Review of World
Economics,143(2), 201226. https://doi.org/10.1007/s10290-007-0105-0
Finger, J. M., & Kreinin, M. E. (1979). A measure of export similarity and its possible uses. The Economic Journal,
89(356), 905912. https://doi.org/10.2307/2231506
Franzen, A. (2003). Environmental attitudes in international comparison: An analysis of the ISSP surveys 1993 and
2000. Social Science Quarterly,84(2), 297308. https://doi.org/10.1111/1540-6237.8402005
Greenaway, D., Mahabir, A., & Milner, C. (2008). Has China displaced other Asian countriesexports? China Econ-
omic Review,19(2), 152169. https://doi.org/10.1016/j.chieco.2007.11.002
Hallak, J. C. (2006). Product quality and the direction of trade. Journal of International Economics,68(1), 238265.
https://doi.org/10.1016/j.jinteco.2005.04.001
Hamilton, S. F., & Zilberman, D. (2006). Green markets, ecocertification, and equilibrium fraud. Journal of Envi-
ronmental Economics and Management,52(3), 627644. https://doi.org/10.1016/j.jeem.2006.05.002
Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of Economic Growth,12(1), 1
25. https://doi.org/10.1007/s10887-006-9009-4
Helpman, E. (1985). Multinational corporations and trade structure. Review of Economic Studies,52(3), 443457.
https://doi.org/10.2307/2297663
Ianchovichina, E., & Walmsley, T. (2005). Impact of Chinas WTO accession on East Asia. Contemporary Econ-
omic Policy,23(2), 261277. https://doi.org/10.1093/cep/byi020
18
|
LA
Jarreau, J., & Poncet, S. (2012). Export sophistication and economic growth: Evidence from China. Journal of
Development Economics,97(2), 281292. https://doi.org/10.1016/j.jdeveco.2011.04.001
Lall, S., & Albaladejo, M. (2004). Chinas competitive performance: A threat to East Asian manufactured exports?
World Development,32(9), 14411466. https://doi.org/10.1016/j.worlddev.2004.03.006
Minondo, A. (2010). Exportsqualityadjusted productivity and economic growth. The Journal of International
Trade and Economic Development,19(2), 257287. https://doi.org/10.1080/09638190802573071
Shafaeddin, S. M. (2004). Is Chinas accession to WTO threatening exports of developing countries? China Econ-
omic Review,15(2), 109144. https://doi.org/10.1016/j.chieco.2003.09.003
Torgler, B., & Garcia-Valinas, M. A. (2007). The determinants of individualsattitudes towards preventing environ-
mental damage. Ecological Economics,63(23), 536552. https://doi.org/10.1016/j.ecolecon.2006.12.013
Van Beers, C., & van den Bergh, J. C. J. M. (1997). An empirical multicountry analysis of the impact of environ-
mental regulations on foreign trade flows. Kyklos,50(1), 2946. https://doi.org/10.1111/1467-6435.00002
Xu, B., & Lu, J. (2009). Foreign direct investment, processing trade, and the sophistication of Chinas exports.
China Economic Review,20(3), 425439. https://doi.org/10.1016/j.chieco.2009.01.004
Yao, S. (2009). Why are Chinese exports not so special? China and World Economy,17(1), 4765. https://doi.org/
10.1111/j.1749-124X.2009.01130.x
How to cite this article: La, J. J. Effects of the preference for environmental quality on the
export competition between China and OECD countries. World Econ. 2018;00:120.
https://doi.org/10.1111/twec.12732
APPENDIX A
TABLE A1 Country coverage
Exporters (30) Importers (60)
Australia Argentina Latvia
Austria Australia Lebanon
Belgium Austria Lithuania
Canada Belarus Luxembourg
Czech Rep. Belgium Malaysia
Denmark Brazil Malta
Finland Bulgaria Mexico
France Canada Netherlands
Germany Chile New Zealand
Greece Hong Kong Norway
Hungary Colombia Oman
Iceland Croatia Philippines
Ireland Cyprus Poland
Italy Czech Rep. Portugal
Japan Denmark South Korea
Luxembourg Ecuador Romania
(Continues)
LA
|
19
TABLE A1 (Continued)
Exporters (30) Importers (60)
Mexico Estonia Russian Federation
Netherlands Finland Saudi Arabia
New Zealand France Singapore
Norway Germany Slovakia
Poland Greece Slovenia
Portugal Hungary South Africa
South Korea Iceland Spain
Slovakia India Sweden
Spain Indonesia Switzerland
Sweden Ireland Thailand
Switzerland Israel Turkey
Turkey Italy UK
UK Japan USA
USA Jordan Vietnam
APPENDIX B
REGRESSION RESULTS FOR CAPITAL GOODS
For capital goods, the coefficient of lnCis significantly positive at the 5% level, whereas that of the
interaction term between lnCand lnIPEQI is significantly negative at the 1% level. More specifically,
the negative slope of the lnCcurve becomes steeper as the value of lnIPEQI increases, with 0.304,
0.315, 0.318, 0.320 and 0.330 corresponding to the minimum (4.239), 1st quartile (4.289), 2nd
quartile (4.302), 3rd quartile (4.311) and 4th quartile (4.355) of the lnIPEQI, respectively. This result
confirms the expectation in Section 2 that the crowdingout effect of Chinas export growth on the
exports of OECD countries is the most severe in this sector. However, as capital goods are included
in the nonenvironmental demand sector, the variable for lnIPEQI cannot be regarded as reflecting an
importers preference for environmental quality, as explained in Section 3. Rather, this value repre-
sents the level of the importing countrys income, given that the correlation coefficient between the
logarithm of the average IPEQI for capital goods and the logarithm of the average income from 2000
to 2010 through 99 observations is 0.67 at the 1% significance level. Thus, it is expected that import-
ing countries with higher incomes prefer exports from China to those from OECD countries because
these countries are willing to obtain cheaper capital goods to cover their higher labour costs. Further-
more, it is predicted that richer countries are better able to use Chinas capital goods as a result of
their better production technology and infrastructure to produce highquality consumption goods or
intermediate goods. The positive relationship between income and production process sophistication
supports this argument. The correlation coefficient between the average income and the average level
of production process sophistication from 2007 to 2010 through 118 observations is 0.8035 at the 1%
significance level.
17
17
The global competitiveness index published by World Economic Forum offers the production process sophistication index
measuring to what extent firms in a country use the latest technologies for production.
20
|
LA
... Çin, OECD ülkelerinden daha çok ara mal ve sermaye malı alarak ihracatındaki büyüme hızını artırmaktadır. Küre-sel üretimde de düşük katma değerli ürünlerden yüksek katma değerli ürünlere doğru bir geçiş yaptığı da gözlenmektedir (La, 2019(La, :1180. ...
Chapter
Full-text available
ÖZET 2008 Küresel Ekonomik Krizinin ardından, ağırlıklı olarak G20 ülkeleri arasında cereyan eden ticari gerilimlerin ekonomi gündemine egemen olduğu ve bir bütün ola-rak dünya ekonomisini çevreleyen belirsizliği artırdığı görülmektedir. Öte yandan ABD’nin ulusal güvenlik gerekçesine dayanarak 2018 yılından itibaren uygulamaya koyduğu ve başta Çin olmak üzere, ikili ticarette açık verdiği ülkeleri hedef aldığı tarife artışları, tarifelerden olumsuz etkilenen ülkelerin, özellikle Çin’in misillemeleri sonucunda bir ticaret savaşına dönüşmüştür. Gelinen noktada küresel üretimin ve ticaretin entegrasyon süreci, durma ve hatta tersine gitme riskiyle karşı karşıya bu-lunmaktadır. Bu çalışmada, 21. yüzyılın ekonomi politiğinin en belirgin olgularından biri olan ticaret savaşının gelişimi ve dünya ekonomisine yansımaları incelenmiştir. Ulaşılan bulgular, mevcut ticaret savaşı çerçevesinde hem tarife artışlarını gerçekleş-tiren ülkelerde hem de tarife artışlarına maruz kalan ülkelerde yatırım, istihdam ve reel gelir kayıplarına işaret etmektedir. Anahtar Sözcükler: Uluslararası Ticaret, Korumacılık, Ticaret Savaşı, ABD, Çin, Dünya Ekonomisi. ABSTRACT After the 2008 Global Economic Crisis, trade tensions happening mainly among G20 countries dominate the economic agenda and increased the uncertainty sur-rounding the world economy as a whole. On the other hand, the tariff increases, which the US has been implementing since 2018 on the grounds of national security and targeting the countries which the US has a deficit in bilateral trade, especially China, turned into a trade war as a result of the retaliations of the countries affected by tar-iffs, especially China. At this point, the integration process of global production and trade is at risk of stopping and even reversing. In this study, the development of trade war which is one of the most prominent phenomena of economic policy of 21st century and its reflections on world economy are examined. The findings indicate investment, employment and real income losses both in the countries performing tariff increases and in countries subject to tariff increases within the framework of the current trade war. Key Words: International Trade, Protectionism, Trade War, USA, China, Global Economy
... Çin, OECD ülkelerinden daha çok ara mal ve sermaye malı alarak ihracatındaki büyüme hızını artırmaktadır. Küre-sel üretimde de düşük katma değerli ürünlerden yüksek katma değerli ürünlere doğru bir geçiş yaptığı da gözlenmektedir (La, 2019(La, :1180. ...
Article
Full-text available
According to recent studies, countries specialised in products associated with higher productivity levels are likely to grow faster than countries specialised in other goods. A limitation of these studies is that they do not control for quality differences within a product category when measuring goods' productivity level. In this paper we show that if products are distinguished by quality level there is no longer a robust relationship between specialising in products associated with higher productivity levels and faster growth. On the contrary, we find it is the specialisation in products that allow a larger room for quality improvement that leads to higher economic growth.
Article
Full-text available
We analyze the impact of China’s growth on the exports of other Asian countries, distinguishing China’s demand for imports from its penetration of export markets. We account for the endogeneity of Chinese exports by applying instrumental variables in a gravity model with country-pair fixed-effects. We find that China’s crowding-out effect is felt mainly in markets for consumer goods and hence by less-developed Asian countries, not in markets for capital goods or by the more advanced Asian economies. Meanwhile, China has been sucking in imports from its Asian neighbors, but this effect is mainly felt in markets for capital goods. Hence, more and less developed Asian countries are being affected very differently by China’s rise.
Article
Full-text available
We examine China's competitive threat to East Asian neighbors in the 1990s, benchmarking performance by technology and market. Market share losses are mainly in low-technology products; Japan is the most vulnerable market. China and its neighbors are raising high-technology exports in tandem: international production systems here are leading to complementarity rather than confrontation. In direct trade with its neighbors, China is acting as an engine of export growth, with imports outpacing exports. This may change, however, as China climbs the value chain and takes over activities that have driven East Asian export growth even within integrated production systems.
Article
Full-text available
This paper investigates empirically the determinants of individuals' attitudes towards preventing environmental damage in Spain using data from the World Values Survey and European Values Survey for the periods 1990, 1995 and 1999/2000. Compared to many previous studies, we present a richer set of independent variables and found that strongly neglected variables such as political interest and social capital have a strong impact on individuals' preferences to prevent environmental damage. An interesting aspect in our study is the ability to investigate environmental preferences over time. The results show strong differences over time. Finally, using disaggregated data for Spanish regions, we also find significant regional differences.
Article
We consider the effect of export sophistication on economic performance by appealing to regional variation within one single country (China) over the 1997–2009 period. We find evidence in support of Hausmann, Hwang and Rodrik (2007), in that regions specializing in more sophisticated goods subsequently grow faster. We find substantial variation in export sophistication at the province and prefecture level, controlling for the level of development, and that this sophistication in turn drives growth. Our results suggest that these gains are limited to the ordinary export activities undertaken by domestic firms: no direct gains result from either processing trade activities or foreign firms, even though these are the main contributors to the global upgrading of China's exports. As such, the extent of assembly trade and foreign entities should be distinguished in order to measure the true movement in a country's technology and the contribution of exports to economic growth.
Article
Green product development, which addresses environmental issues through product design and innovation as opposed to the traditional end-of-pipe-control approach, is receiving significant attention from customers, industries, and governments around the world. In this paper we develop a quality-based model for analyzing the strategic and policy issues concerning the development of products with conflicting traditional and environmental attributes. On the demand side of the problem, we use the framework of conjoint analysis to structure the preferences of the ordinary and green customers. On the supply side, we apply the theories in optimal product design and market segmentation to analyze the producer's strategic decisions regarding the number of products introduced and their prices and qualities. On the policy side, we evaluate the effects of environmental standards on the economic and environmental consequences of green product development. By jointly considering the interactions among the customers' preferences, the producer's product strategies, and the environmental standards imposed by governments, we present some interesting findings that can be used to manage and regulate the development of green products. Two major findings show that green product development and stricter environmental standards might not necessarily benefit the environment.
Article
Summary Most of the previous studies on the trade effects of intellectual property rights (IPR) protection have been from the perspective of major industrialized nations. However, much of the current debate on the effects of IPR protection involves large developing countries. This study contributes to the literature by analyzing the impact of stronger IPR laws in China on its bilateral trade flows. We estimate the effects of IPR protection on China's imports at the aggregate and detailed product categories for both developed and developing countries. The empirical results suggest that increased IPR protection stimulates China's imports, particularly for knowledge-intensive products.
Article
The industrialized and newly industrializing economies (NIEs) in East Asia will benefit from China's WTO accession, and the developing economies in the region may incur small welfare losses. China will increase its demand for high-end manufacturing products from Japan and the NIEs and farm products, natural resources, and manufactured goods from developing East Asia. New foreign investment may flow into these expanding sectors. The overall impact on foreign investment is likely to be positive in the NIEs but negative in developing East Asia. The NIEs may face heightened competition in global markets as China's comparative advantage shifts into high-end products. (JEL "F11", "F13", "F15") Copyright 2005 Western Economic Association International.
Article
This paper uses gravity modelling to explore whether and how the growth of China's exports is displacing exports of other Asian countries to third markets over the period 1990–2003. Chinese exports are defined both narrowly and more broadly to include exports from Hong Kong. We investigate whether the displacement effect on Asian exports differs when exports from Hong Kong and China are combined compared to the narrow case of Chinese exports only. Aggregate and disaggregated analyses are undertaken. In the latter, we explore whether the displacement effect varies across Asian countries and in trade with different types of countries. We find evidence of a displacement effect which is more pronounced in developed markets and stronger for Hong Kong and China combined. Further it is the high income Asian exporters that experienced a greater displacement effect. We also investigate whether China's development has generated any offsetting effects on its neighbours' exports to China itself and find that Chinese growth has indeed increased China's imports from the Asian countries in the sample and in particular from Japan and Korea.
Article
China's export structure has shown a rapid shift towards more sophisticated industries. While some believe that this trend is a result of processing trade and foreign direct investment, the evidence is mixed. This paper examines variations in level of export sophistication across China's manufacturing industries. We find that an industry's level of export sophistication is positively related to the share of wholly foreign owned enterprises from OECD countries and the share of processing exports of foreign-invested enterprises, and negatively related to the share of processing exports of indigenous Chinese enterprises. Evidence from the relative export prices of Chinese goods, which measure within-product export sophistication, shows a similar pattern.